SUMMARY Stem cell differentiation takes place on a complex landscape defined by a continuum of intermediate cell states, branching trajectories, and highly coordinated regulatory dynamics. This complexity is often hidden to ensemble measurements, which are typically limited to measuring average values of markers in populations of cells. The recent and rapid emergence of high-throughput single-cell RNA sequencing has provided a powerful solution for dissecting transitions between cell states by measuring the expression of thousands of genes in large populations of cells. Complementary statistical tools can process these large data sets and identify distinct groups of cells base on their transcriptional profile. However, RNA transcript abundance does not always correlate with protein composition and there are many important intracellular molecules, like lipids and metabolites, which are not specifically encoded in the genome. Orthogonal molecular measurements of cell state could provide a powerful complement to transcriptome analysis for investigating regulatory dynamics during stem cell differentiation. This research program focuses on the development of technology to facilitate ?multi-omic? measurements in single cells. We take advantage of three core technologies to measure cell state phenotypes. We use DamID to probe genome organization and protein-DNA interactions, single-cell RNAseq to provide whole-transcriptome gene expression profiling, and coherent Raman imaging to characterize the chemical composition of live cells. Microfluidic technology facilitates the integration of these techniques and enables multimodal measurement of single cells. This platform will provide a new approach for dissecting coordinated regulatory networks in differentiating stem cells. Our ultimate goal is to develop a tool to make all of these measurements in situ, in order to retain single-cell spatial information and cellular context in a developing tissue or whole organism.